Metaheuristic Approaches for Test Redundancy Reduction: A Systematic Review of Algorithms, Optimization Strategies, and Research Trends

Test redundancy reduction (TRR) addresses the continued growth of regression test suites by selecting a smaller test set while preserving required testing adequacy. This updated systematic review maps metaheuristic approaches to TRR and closely related test-suite reduction/minimization across algorithm family, search architecture, task structure, objective structure, evaluation practice, and reproducibility. A seeded-update review design was used: the test-suite-reduction evidence base reported by Habib et al. was re-screened, followed by targeted bibliographic searching and backward and forward snowballing to update the evidence through September 2026. The quantitative corpus contains 48 peer-reviewed primary studies published from 2010 to 2026. Thirty-three studies (68.8%) use single or modified-single architectures, while 15 (31.3%) use hybrid architectures. Swarm-intelligence methods account for 24 studies (50.0%). Multi-objective optimization appears in 18 studies (37.5%), whereas 30 (62.5%) use single-objective formulations. Six studies (12.5%) explicitly employ multi-task or multifactorial optimization, with this cluster emerging in 2024–2025, while single-task formulations remain dominant (42/48, 87.5%). The review therefore reframes multi-task TRR from an absent direction to a recent but still comparatively underrepresented research direction. Across the corpus, direct algorithm ranking remains inappropriate because benchmark suites, adequacy criteria, search budgets, parameter reporting, and runtime environments differ substantially. The review further distinguishes algorithm family from search architecture, task structure, and objective structure so that hybrid, multi-objective, and multi-task formulations are not conflated. The resulting evidence map provides an auditable basis for future hybrid, multi-objective, and multi-task TRR research.

test redundancy reduction, test suite minimization, metaheuristics, hybrid optimization

1. Ahmed, B. S. (2016). Test case minimization approach using fault detection and combinatorial optimization techniques for configuration-aware structural testing. Engineering Science and Technology, an International Journal, 19(2), 737–753. https://doi.org/10.1016/j.jestch.2015.11.006 [Google Scholar] [Crossref]

2. Ahuja, N., & Bhatia, P. K. (2023). An improved artificial bee colony optimization algorithm for test suite minimization. International Journal of Advanced Computer Science and Applications, 14(7). https://doi.org/10.14569/IJACSA.2023.0140774 [Google Scholar] [Crossref]

3. Ahuja, N., & Bhatia, P. K. (2024). Test suite minimization through PSO. 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). https://doi.org/10.1109/ICRITO61523.2024.10522114 [Google Scholar] [Crossref]

4. Anwar, Z., Afzal, H., Bibi, N., Abbas, H., Mohsin, A., & Arif, O. (2019). A hybrid-adaptive neuro-fuzzy inference system for multi-objective regression test suites optimization. Neural Computing and Applications, 31(11), 7287–7301. https://doi.org/10.1007/s00521-018-3560-8 [Google Scholar] [Crossref]

5. Asthana, M., Gupta, K. D., & Kumar, A. (2020). Test suite optimization using lion search algorithm. In Ambient Communications and Computer Systems (pp. 77–90). Springer. https://doi.org/10.1007/978-981-15-1518-7_7 [Google Scholar] [Crossref]

6. Bajaj, A., & Abraham, A. (2021). Prioritizing and minimizing the test cases using the dragonfly algorithms. International Journal of Computer Information Systems and Industrial Management Applications, 13, 62–71. [Google Scholar] [Crossref]

7. Bajaj, A., Sangwan, O. P., & Abraham, A. (2022). Improved novel bat algorithm for test case prioritization and minimization. Soft Computing, 26(22), 12393–12419. https://doi.org/10.1007/s00500-022-07121-9 [Google Scholar] [Crossref]

8. Bharathi, M., & Sangeetha, V. (2018). Weighted rank ant colony metaheuristics optimization based test suite reduction in combinatorial testing for improving software quality. 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS). https://doi.org/10.1109/ICCONS.2018.8663102 [Google Scholar] [Crossref]

9. Bharathi, M. (2022). Hybrid particle swarm and ranked firefly metaheuristic optimization-based software test case minimization. International Journal of Applied Metaheuristic Computing, 13(1), 1–20. https://doi.org/10.4018/IJAMC.290534 [Google Scholar] [Crossref]

10. Brereton, P., Kitchenham, B. A., Budgen, D., Turner, M., & Khalil, M. (2007). Lessons from applying the systematic literature review process within the software engineering domain. Journal of Systems and Software, 80(4), 571–583. https://doi.org/10.1016/j.jss.2006.07.009 [Google Scholar] [Crossref]

11. Chintapalli, S. S., & Singh, S. P. (2019). Flower pollination algorithm for effective test case optimization in software testing. International Journal of Engineering and Advanced Technology, 9(1), 4711–4716. https://doi.org/10.35940/ijeat.A1983.109119 [Google Scholar] [Crossref]

12. Choudhary, A., Agrawal, A. P., & Kaur, A. (2018). An effective approach for regression test case selection using Pareto based multi-objective harmony search. Proceedings of the 11th International Workshop on Search-Based Software Testing, 13–20. https://doi.org/10.1145/3194718.3194722 [Google Scholar] [Crossref]

13. Deneke, A., Assefa, B. G., & Mohapatra, S. K. (2022). Test suite minimization using particle swarm optimization. Materials Today: Proceedings, 60(Part 1), 229–233. https://doi.org/10.1016/j.matpr.2021.12.472 [Google Scholar] [Crossref]

14. Freeda, A. R., & Rajendran, S. P. (2024). Test suite optimization under multi-objective constraints for software fault detection and localization: Hybrid optimization based model. Web Intelligence, 22(2). https://doi.org/10.3233/WEB-220131 [Google Scholar] [Crossref]

15. Gan, N., Wang, H., Zhao, Y., & Yang, F. (2021). Test case set reduction method based on GRE and ant colony algorithm. 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), 417–421. [Google Scholar] [Crossref]

16. Geng, J., Li, Z., Zhao, R., & Guo, J. (2016). Search based test suite minimization for fault detection and localization: A co-driven method. In Search Based Software Engineering (LNCS 9962, pp. 34–48). Springer. https://doi.org/10.1007/978-3-319-47106-8_3 [Google Scholar] [Crossref]

17. Gupta, N., Sharma, A., & Pachariya, M. K. (2022). Multi-objective test suite optimization for detection and localization of software faults. Journal of King Saud University - Computer and Information Sciences, 34(6), 2897–2909. https://doi.org/10.1016/j.jksuci.2020.01.009 [Google Scholar] [Crossref]

18. Habib, A. S., Khan, S. U. R., & Felix, E. A. (2023). A systematic review on search-based test suite reduction: State-of-the-art, taxonomy, and future directions. IET Software, 17(2), 93–136. https://doi.org/10.1049/sfw2.12104 [Google Scholar] [Crossref]

19. Habib, A. S., Khan, S. U. R., Hussain, S., Ibrahim, N., Nisa, H. U., & Yousafzai, A. (2024). A similarity-based multi-objective test optimization technique using search algorithm. Systems and Soft Computing, 6, 200164. https://doi.org/10.1016/j.sasc.2024.200164 [Google Scholar] [Crossref]

20. Jeyaprakash, S., & Alagarsamy, K. (2015). A distinctive genetic approach for test-suite optimization. Procedia Computer Science, 62, 427–434. https://doi.org/10.1016/j.procs.2015.08.437 [Google Scholar] [Crossref]

21. Kabir, M. N., Ali, J., Alsewari, A. R. A., & Zamli, K. Z. (2017). An adaptive flower pollination algorithm for software test suite minimization. 2017 3rd International Conference on Electrical Information and Communication Technology (EICT), 1–5. https://doi.org/10.1109/EICT.2017.8275215 [Google Scholar] [Crossref]

22. Kader, M. A., Zamli, K. Z., Mohamad, A. B., & Mojumder, M. A. (2025). A two-phase adaptive hybrid SCA-PSO strategy for efficient multi-task test redundancy reduction. 2025 IEEE 9th International Conference on Software Engineering and Computer Systems (ICSECS), 46–51. https://doi.org/10.1109/ICSECS65227.2025.11279040 [Google Scholar] [Crossref]

23. Kang, H. Y., Raja Ikram, R. R., Zamli, K. Z., & Emran, N. A. (2025). Exploring multifactorial techniques in rat swarm optimization: Preliminary results. Engineering, Technology & Applied Science Research, 15(3), 23430–23435. https://doi.org/10.48084/etasr.10690 [Google Scholar] [Crossref]

24. Khan, S. U. R., Lee, S. P., Ahmad, R. W., Akhunzada, A., & Chang, V. (2016). A survey on test suite reduction frameworks and tools. International Journal of Information Management, 36(6), 963–975. https://doi.org/10.1016/j.ijinfomgt.2016.05.025 [Google Scholar] [Crossref]

25. Khan, S. U. R., Lee, S. P., Javaid, N., & Abdul, W. (2018). A systematic review on test suite reduction: Approaches, experiment quality evaluation, and guidelines. IEEE Access, 6, 11816–11841. https://doi.org/10.1109/ACCESS.2018.2809600 [Google Scholar] [Crossref]

26. Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. EBSE Technical Report EBSE-2007-01. [Google Scholar] [Crossref]

27. Lakshminarayana, P., & SureshKumar, T. V. (2021). Automatic generation and optimization of test case using hybrid cuckoo search and bee colony algorithm. Journal of Intelligent Systems, 30(1), 59–72. https://doi.org/10.1515/jisys-2019-0051 [Google Scholar] [Crossref]

28. Mekeng, A. M. R., Zamli, K. Z., Zarlis, M., Mohamad, M. A., & Darusalam, U. (2026). Tie-breaking in social network search algorithm for software test redundancy reduction optimization problem. 2026 International Seminar on Intelligent Business and Edge-Computing Research (ISIBER), 494–499. https://doi.org/10.1109/ISIBER68248.2026.11470036 [Google Scholar] [Crossref]

29. Mohanty, S., Mohapatra, S. K., & Meko, S. F. (2020). Ant colony optimization (ACO-Min) algorithm for test suite minimization. In Progress in Computing, Analytics and Networking (pp. 55–63). Springer. https://doi.org/10.1007/978-981-15-2414-1_6 [Google Scholar] [Crossref]

30. Mojumder, M. A., Qasymeh, M., Zamli, K. Z., Mistarihi, M., & Kader, M. A. (2025). A relay-based hybrid PSO-Jaya algorithm for efficient test redundancy reduction. 2025 IEEE 9th International Conference on Software Engineering and Computer Systems (ICSECS), 209–213. https://doi.org/10.1109/ICSECS65227.2025.11279093 [Google Scholar] [Crossref]

31. Nachiyappan, S., Vimaladevi, A., & SelvaLakshmi, C. (2010). An evolutionary algorithm for regression test suite reduction. 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 503–508. [Google Scholar] [Crossref]

32. Panigrahi, S. S., Sahoo, P. K., Sahu, B. P., Panigrahi, A., & Jena, A. K. (2021). Model-driven automatic paths generation and test case optimization using hybrid FA-BC. 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), 263–268. https://doi.org/10.1109/ESCI50559.2021.9396999 [Google Scholar] [Crossref]

33. Rahman, M., Zamli, K. Z., & Kader, M. A. (2024). Improved sparrow search algorithm for test redundancy reduction. Lecture Notes in Electrical Engineering, 1212, 33–45. https://doi.org/10.1007/978-981-97-3847-2_4 [Google Scholar] [Crossref]

34. Rahman, M., Zamli, K. Z., Kader, M. A., Sidek, R. M., & Din, F. (2024). Comprehensive review on the state-of-the-arts and solutions to the test redundancy reduction problem with taxonomy. Journal of Advanced Research in Applied Sciences and Engineering Technology, 35(1), 62–87. https://doi.org/10.37934/araset.34.3.6287 [Google Scholar] [Crossref]

35. Reda, N., Hamdy, A., & Rashed, E. A. (2022). Multi-objective adapted binary bat for test suite reduction. Intelligent Automation & Soft Computing, 31(2), 781–797. https://doi.org/10.32604/iasc.2022.019669 [Google Scholar] [Crossref]

36. Rizwan, A., Zamli, K. Z., Jilani, A. A. A., Ghori, M. R., & Masood, Z. (2025). Multitask strategy for test redundancy reduction optimization. 2025 IEEE 9th International Conference on Software Engineering and Computer Systems (ICSECS), 394–399. https://doi.org/10.1109/ICSECS65227.2025.11279026 [Google Scholar] [Crossref]

37. Sahoo, R. K., Mohapatra, D. P., & Patra, M. R. (2016). A firefly algorithm based approach for automated generation and optimization of test cases. International Journal of Computer Sciences and Engineering, 4(8), 54–58. [Google Scholar] [Crossref]

38. Selvakumar, S., Manikumar, T., Kumar, A. J. S., & Latha, L. (2013). An assimilated approach of concept analysis and particle swarm optimization algorithm for effective test suite minimization. 2013 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). https://doi.org/10.1109/ICCIC.2013.6724231 [Google Scholar] [Crossref]

39. Sharma, A., Jadhav, A., Srivastava, P. R., & Goyal, R. (2010). Test cost optimization using tabu search. Journal of Software Engineering and Applications, 3(5), 477–486. https://doi.org/10.4236/jsea.2010.35054 [Google Scholar] [Crossref]

40. Sheikh, R., Babar, M. I., Butt, R., Abdelmaboud, A., & Eisa, T. A. E. (2023). An optimized test case minimization technique using genetic algorithm for regression testing. Computers, Materials & Continua, 74(3), 6789–6806. https://doi.org/10.32604/cmc.2023.028625 [Google Scholar] [Crossref]

41. Sidek, R. M., Zamli, K. Z., & Kader, M. A. (2025a). Preliminary study of Brown Bear and Roulette Wheel Brown Bear for test redundancy reduction optimization problem. 2025 IEEE 9th International Conference on Software Engineering and Computer Systems (ICSECS), 57–61. https://doi.org/10.1109/ICSECS65227.2025.11279267 [Google Scholar] [Crossref]

42. Sidek, R. M., Zamli, K. Z., & Mohamed, R. (2025b). Multi-task test redundancy reduction optimization problem based on the Brown Bear Algorithm. Proceedings of the 14th International Conference on Software and Computer Applications (ICSCA), 120–125. https://doi.org/10.1145/3731806.3731865 [Google Scholar] [Crossref]

43. Sugave, S. R., Patil, S. H., & Reddy, B. E. (2017). DDF: Diversity Dragonfly Algorithm for cost-aware test suite minimization approach for software testing. 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 701–707. https://doi.org/10.1109/ICCONS.2017.8250554 [Google Scholar] [Crossref]

44. Sun, J.-Z., & Wang, S.-Y. (2010). A novel chaos discrete particle swarm optimization algorithm for test suite reduction. The 2nd International Conference on Information Science and Engineering, 1–4. https://doi.org/10.1109/ICISE.2010.5689888 [Google Scholar] [Crossref]

45. Suri, B., Mangal, I., & Srivastava, V. (2011). Regression test suite reduction using a hybrid technique based on BCO and genetic algorithm. Special Issue of International Journal of Computer Science and Informatics, II(1). https://doi.org/10.47893/IJCSI.2013.1113 [Google Scholar] [Crossref]

46. Turner, A. J., White, D. R., & Drake, J. H. (2016). Multi-objective regression test suite minimisation for Mockito. In Search Based Software Engineering (LNCS 9962, pp. 244–249). Springer. https://doi.org/10.1007/978-3-319-47106-8_19 [Google Scholar] [Crossref]

47. Verma, A. S., Choudhary, A., & Tiwari, S. (2020). Test case optimization using Butterfly Optimization Algorithm. 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 704–709. https://doi.org/10.1109/Confluence47617.2020.9058334 [Google Scholar] [Crossref]

48. Wei, W., Cheng, W., Ye, L., Xia, S., Wang, Y., Xing, Y., & Wang, X. (2022). A study of test suite reduction based on Ant Lion Optimizer. Tehnički vjesnik, 29(1), 246–251. https://doi.org/10.17559/TV-20210807090754 [Google Scholar] [Crossref]

49. Yamuç, A., Cingiz, M. Ö., Biricik, G., & Kalıpsız, O. (2017). Solving test suite reduction problem using greedy and genetic algorithms. 2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–5. https://doi.org/10.1109/ECAI.2017.8166445 [Google Scholar] [Crossref]

50. Yoo, S., & Harman, M. (2012). Regression testing minimization, selection and prioritization: A survey. Software Testing, Verification and Reliability, 22(2), 67–120. https://doi.org/10.1002/stvr.430 [Google Scholar] [Crossref]

51. Zamli, K. Z., Hassin, M. H. M., & Al-Kazemi, B. (2015). tReductSA – test redundancy reduction strategy based on simulated annealing. Communications in Computer and Information Science, 513, 223–236. https://doi.org/10.1007/978-3-319-17530-0_16 [Google Scholar] [Crossref]

52. Zamli, K. Z., Safieny, N., & Din, F. (2018). Hybrid test redundancy reduction strategy based on Global Neighborhood Algorithm and Simulated Annealing. Proceedings of the 2018 7th International Conference on Software and Computer Applications, 87–91. https://doi.org/10.1145/3185089.3185146 [Google Scholar] [Crossref]

53. Zamli, K. Z., Kader, M. A., & Mekeng, A. M. R. (2024). A population division based multi-task sine cosine algorithm for test redundancy reduction optimization. Proceedings of the 2024 10th International Conference on Computer Technology Applications (ICCTA), 94–102. https://doi.org/10.1145/3674558.3674571 [Google Scholar] [Crossref]

54. Zamli, K. Z., Kader, M. A., & Sidek, R. M. (2025). Multi-task test redundancy reduction optimization problem based on the Sine Cosine Algorithm. Lecture Notes in Networks and Systems, 1364, 189–201. https://doi.org/10.1007/978-981-96-5220-4_16 [Google Scholar] [Crossref]

55. Zhang, Y.-N., Yang, H., Lin, Z.-K., Dai, Q., & Li, Y.-F. (2017). A test suite reduction method based on novel quantum ant colony algorithm. 2017 4th International Conference on Information Science and Control Engineering (ICISCE), 825–829. https://doi.org/10.1109/ICISCE.2017.176 [Google Scholar] [Crossref]

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